Cytomining Ecosystem Software Landscape Analysis: Data Profile Report

Dataset statistics

Number of variables	26
Number of observations	1123
Missing cells	1970
Missing cells (%)	6.7%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	220.6 KiB
Average record size in memory	201.1 B

Variable types

Text	5
Numeric	9
Unsupported	7
Categorical	2
DateTime	2
Boolean	1

Alerts

`GitHub Repository ID` is highly overall correlated with `Duration Created to Now in Years`	High correlation
`GitHub Stars` is highly overall correlated with `GitHub Forks` and 4 other fields	High correlation
`GitHub Forks` is highly overall correlated with `GitHub Stars` and 4 other fields	High correlation
`GitHub Subscribers` is highly overall correlated with `GitHub Stars` and 4 other fields	High correlation
`GitHub Open Issues` is highly overall correlated with `GitHub Stars` and 4 other fields	High correlation
`GitHub Contributors` is highly overall correlated with `GitHub Stars` and 4 other fields	High correlation
`GitHub Network Count` is highly overall correlated with `GitHub Stars` and 4 other fields	High correlation
`Duration Created to Now in Years` is highly overall correlated with `GitHub Repository ID`	High correlation
`GitHub Repo Archived` is highly imbalanced (90.9%)	Imbalance
`category` is highly imbalanced (75.0%)	Imbalance
`Project Homepage` has 539 (48.0%) missing values	Missing
`GitHub License Type` has 551 (49.1%) missing values	Missing
`GitHub Description` has 54 (4.8%) missing values	Missing
`GitHub Organization` has 826 (73.6%) missing values	Missing
`GitHub Stars` is highly skewed (γ1 = 21.55490466)	Skewed
`GitHub Forks` is highly skewed (γ1 = 25.3051631)	Skewed
`GitHub Subscribers` is highly skewed (γ1 = 22.78021783)	Skewed
`GitHub Network Count` is highly skewed (γ1 = 25.30491864)	Skewed
`GitHub Repository ID` has unique values	Unique
`Project Repo URL` has unique values	Unique
`Date Created` has unique values	Unique
`Date Most Recent Commit` has unique values	Unique
`Project Landscape Category` is an unsupported type, check if it needs cleaning or further analysis	Unsupported
`GitHub Topics` is an unsupported type, check if it needs cleaning or further analysis	Unsupported
`GitHub Detected Languages` is an unsupported type, check if it needs cleaning or further analysis	Unsupported
`Duration Created to Most Recent Commit` is an unsupported type, check if it needs cleaning or further analysis	Unsupported
`Duration Created to Now` is an unsupported type, check if it needs cleaning or further analysis	Unsupported
`Duration Most Recent Commit to Now` is an unsupported type, check if it needs cleaning or further analysis	Unsupported
`Negative Duration Most Recent Commit to Now` is an unsupported type, check if it needs cleaning or further analysis	Unsupported
`GitHub Stars` has 44 (3.9%) zeros	Zeros
`GitHub Forks` has 435 (38.7%) zeros	Zeros
`GitHub Subscribers` has 52 (4.6%) zeros	Zeros
`GitHub Open Issues` has 674 (60.0%) zeros	Zeros
`GitHub Contributors` has 27 (2.4%) zeros	Zeros
`GitHub Network Count` has 435 (38.7%) zeros	Zeros

Reproduction

Analysis started	2023-10-16 17:47:39.523045
Analysis finished	2023-10-16 17:47:47.149763
Duration	7.63 seconds
Software version	ydata-profiling vv4.6.0
Download configuration	config.json

Project Name
Text

Distinct	1090
Distinct (%)	97.1%
Missing	0
Missing (%)	0.0%
Memory size	8.9 KiB

Length

Max length	100
Median length	65
Mean length	15.050757
Min length	3

Characters and Unicode

Total characters	16902
Distinct characters	65
Distinct categories	6 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1065 ?
Unique (%)	94.8%

Sample

1st row	pandas
2nd row	numpy
3rd row	arrow
4th row	duckdb
5th row	parquet-mr

Value	Count	Frequency (%)
single-cell-analysis	8	0.7%
single-cell-rna-seq-analysis	7	0.6%
single_cell_analysis	7	0.6%
singlecellanalysis	4	0.4%
single-cell-rna-seq	4	0.4%
singlecell	3	0.3%
orchestratingsinglecellanalysis	3	0.3%
single-cell-rna-sequencing-analysis	2	0.2%
cytominer	2	0.2%
spectre	2	0.2%
Other values (1060)	1081	96.3%

Most occurring characters

Value	Count	Frequency (%)
e	1443	8.5%
l	1339	7.9%
s	1259	7.4%
i	1025	6.1%
a	1012	6.0%
n	949	5.6%
-	847	5.0%
c	778	4.6%
o	695	4.1%
t	617	3.7%
Other values (55)	6938	41.0%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	12372	73.2%
Uppercase Letter	2819	16.7%
Dash Punctuation	847	5.0%
Connector Punctuation	431	2.5%
Decimal Number	413	2.4%
Other Punctuation	20	0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	1443	11.7%
l	1339	10.8%
s	1259	10.2%
i	1025	8.3%
a	1012	8.2%
n	949	7.7%
c	778	6.3%
o	695	5.6%
t	617	5.0%
r	573	4.6%
Other values (16)	2682	21.7%

Uppercase Letter

Value	Count	Frequency (%)
A	427	15.1%
C	394	14.0%
S	374	13.3%
R	231	8.2%
N	205	7.3%
M	132	4.7%
T	129	4.6%
P	128	4.5%
I	113	4.0%
D	105	3.7%
Other values (16)	581	20.6%

Decimal Number

Value	Count	Frequency (%)
2	156	37.8%
0	100	24.2%
1	72	17.4%
3	22	5.3%
9	21	5.1%
8	14	3.4%
4	11	2.7%
7	8	1.9%
6	5	1.2%
5	4	1.0%

Dash Punctuation

Value	Count	Frequency (%)
-	847	100.0%

Connector Punctuation

Value	Count	Frequency (%)
_	431	100.0%

Other Punctuation

Value	Count	Frequency (%)
.	20	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	15191	89.9%
Common	1711	10.1%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	1443	9.5%
l	1339	8.8%
s	1259	8.3%
i	1025	6.7%
a	1012	6.7%
n	949	6.2%
c	778	5.1%
o	695	4.6%
t	617	4.1%
r	573	3.8%
Other values (42)	5501	36.2%

Common

Value	Count	Frequency (%)
-	847	49.5%
_	431	25.2%
2	156	9.1%
0	100	5.8%
1	72	4.2%
3	22	1.3%
9	21	1.2%
.	20	1.2%
8	14	0.8%
4	11	0.6%
Other values (3)	17	1.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	16902	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	1443	8.5%
l	1339	7.9%
s	1259	7.4%
i	1025	6.1%
a	1012	6.0%
n	949	5.6%
-	847	5.0%
c	778	4.6%
o	695	4.1%
t	617	3.7%
Other values (55)	6938	41.0%

GitHub Repository ID
Real number (ℝ)

HIGH CORRELATION UNIQUE

Distinct	1123
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2.7923865 × 10⁸

Minimum	858127
Maximum	7.0306429 × 10⁸
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	8.9 KiB

Quantile statistics

Minimum	858127
5-th percentile	48407428
Q1	1.4014133 × 10⁸
median	2.5217638 × 10⁸
Q3	3.8810811 × 10⁸
95-th percentile	6.0591363 × 10⁸
Maximum	7.0306429 × 10⁸
Range	7.0220617 × 10⁸
Interquartile range (IQR)	2.4796679 × 10⁸

Descriptive statistics

Standard deviation	1.7117573 × 10⁸
Coefficient of variation (CV)	0.61300875
Kurtosis	-0.58841698
Mean	2.7923865 × 10⁸
Median Absolute Deviation (MAD)	1.2213142 × 10⁸
Skewness	0.54510555
Sum	3.13585 × 10¹¹
Variance	2.9301131 × 10¹⁶
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
858127	1	0.1%
292773252	1	0.1%
255056675	1	0.1%
146028869	1	0.1%
232201771	1	0.1%
167464808	1	0.1%
156759735	1	0.1%
255893503	1	0.1%
339741407	1	0.1%
188461334	1	0.1%
Other values (1113)	1113	99.1%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
858127	1	0.1%
908607	1	0.1%
925122	1	0.1%
1571820	1	0.1%
2136580	1	0.1%
2290781	1	0.1%
2425273	1	0.1%
4890816	1	0.1%
5771522	1	0.1%
8678018	1	0.1%

Value	Count	Frequency (%)
703064293	1	0.1%
690579583	1	0.1%
689948460	1	0.1%
682462552	1	0.1%
681597755	1	0.1%
679608830	1	0.1%
678475435	1	0.1%
678200716	1	0.1%
677900044	1	0.1%
677883930	1	0.1%

Project Homepage
Text

MISSING

Distinct	206
Distinct (%)	35.3%
Missing	539
Missing (%)	48.0%
Memory size	8.9 KiB

Length

Max length	116
Median length	0
Mean length	13.777397
Min length	0

Characters and Unicode

Total characters	8046
Distinct characters	65
Distinct categories	8 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	204 ?
Unique (%)	34.9%

Sample

1st row	https://pandas.pydata.org
2nd row	https://numpy.org
3rd row	https://arrow.apache.org/
4th row	http://www.duckdb.org
5th row

Value	Count	Frequency (%)
http://nasqar.abudhabi.nyu.edu	2	1.0%
https://combine-lab.github.io/salmon	1	0.5%
https://www.scrna-tools.org	1	0.5%
https://arrow.apache.org	1	0.5%
http://www.duckdb.org	1	0.5%
https://snakemake.readthedocs.io	1	0.5%
http://www.satijalab.org/seurat	1	0.5%
https://napari.org	1	0.5%
https://scanpy.readthedocs.io	1	0.5%
http://scvi-tools.org	1	0.5%
Other values (195)	195	94.7%

Most occurring characters

Value	Count	Frequency (%)
t	757	9.4%
/	727	9.0%
s	489	6.1%
i	475	5.9%
o	461	5.7%
e	413	5.1%
.	408	5.1%
h	406	5.0%
a	366	4.5%
p	308	3.8%
Other values (55)	3236	40.2%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	6072	75.5%
Other Punctuation	1339	16.6%
Decimal Number	291	3.6%
Uppercase Letter	201	2.5%
Dash Punctuation	124	1.5%
Connector Punctuation	17	0.2%
Open Punctuation	1	< 0.1%
Close Punctuation	1	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
t	757	12.5%
s	489	8.1%
i	475	7.8%
o	461	7.6%
e	413	6.8%
h	406	6.7%
a	366	6.0%
p	308	5.1%
c	293	4.8%
r	283	4.7%
Other values (16)	1821	30.0%

Uppercase Letter

Value	Count	Frequency (%)
C	29	14.4%
S	27	13.4%
A	23	11.4%
R	16	8.0%
M	11	5.5%
T	10	5.0%
I	9	4.5%
N	9	4.5%
D	9	4.5%
O	9	4.5%
Other values (12)	49	24.4%

Decimal Number

Value	Count	Frequency (%)
1	63	21.6%
0	58	19.9%
2	42	14.4%
3	23	7.9%
4	22	7.6%
6	19	6.5%
8	17	5.8%
7	16	5.5%
9	16	5.5%
5	15	5.2%

Other Punctuation

Value	Count	Frequency (%)
/	727	54.3%
.	408	30.5%
:	204	15.2%

Dash Punctuation

Value	Count	Frequency (%)
-	124	100.0%

Connector Punctuation

Value	Count	Frequency (%)
_	17	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	1	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	6273	78.0%
Common	1773	22.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
t	757	12.1%
s	489	7.8%
i	475	7.6%
o	461	7.3%
e	413	6.6%
h	406	6.5%
a	366	5.8%
p	308	4.9%
c	293	4.7%
r	283	4.5%
Other values (38)	2022	32.2%

Common

Value	Count	Frequency (%)
/	727	41.0%
.	408	23.0%
:	204	11.5%
-	124	7.0%
1	63	3.6%
0	58	3.3%
2	42	2.4%
3	23	1.3%
4	22	1.2%
6	19	1.1%
Other values (7)	83	4.7%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8046	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
t	757	9.4%
/	727	9.0%
s	489	6.1%
i	475	5.9%
o	461	5.7%
e	413	5.1%
.	408	5.1%
h	406	5.0%
a	366	4.5%
p	308	3.8%
Other values (55)	3236	40.2%

Project Repo URL
Text

UNIQUE

Distinct	1123
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Memory size	8.9 KiB

Length

Max length	157
Median length	91
Mean length	45.211932
Min length	28

Characters and Unicode

Total characters	50773
Distinct characters	67
Distinct categories	6 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1123 ?
Unique (%)	100.0%

Sample

1st row	https://github.com/pandas-dev/pandas
2nd row	https://github.com/numpy/numpy
3rd row	https://github.com/apache/arrow
4th row	https://github.com/duckdb/duckdb
5th row	https://github.com/apache/parquet-mr

Value	Count	Frequency (%)
https://github.com/pandas-dev/pandas	1	0.1%
https://github.com/scverse/scanpy	1	0.1%
https://github.com/duckdb/duckdb	1	0.1%
https://github.com/apache/parquet-mr	1	0.1%
https://github.com/snakemake/snakemake	1	0.1%
https://github.com/satijalab/seurat	1	0.1%
https://github.com/napari/napari	1	0.1%
https://github.com/chris-mcginnis-ucsf/doubletfinder	1	0.1%
https://github.com/theislab/single-cell-tutorial	1	0.1%
https://github.com/sqjin/cellchat	1	0.1%
Other values (1113)	1113	99.1%

Most occurring characters

Value	Count	Frequency (%)
/	4492	8.8%
t	4402	8.7%
i	3032	6.0%
s	2940	5.8%
h	2809	5.5%
o	2442	4.8%
c	2302	4.5%
e	2218	4.4%
a	2211	4.4%
l	1917	3.8%
Other values (57)	22008	43.3%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	37999	74.8%
Other Punctuation	6758	13.3%
Uppercase Letter	3741	7.4%
Dash Punctuation	1105	2.2%
Decimal Number	739	1.5%
Connector Punctuation	431	0.8%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
t	4402	11.6%
i	3032	8.0%
s	2940	7.7%
h	2809	7.4%
o	2442	6.4%
c	2302	6.1%
e	2218	5.8%
a	2211	5.8%
l	1917	5.0%
g	1817	4.8%
Other values (16)	11909	31.3%

Uppercase Letter

Value	Count	Frequency (%)
A	476	12.7%
C	466	12.5%
S	451	12.1%
R	255	6.8%
N	238	6.4%
M	193	5.2%
L	173	4.6%
T	173	4.6%
P	157	4.2%
I	157	4.2%
Other values (16)	1002	26.8%

Decimal Number

Value	Count	Frequency (%)
2	196	26.5%
0	153	20.7%
1	127	17.2%
9	54	7.3%
3	47	6.4%
8	43	5.8%
4	39	5.3%
7	31	4.2%
5	27	3.7%
6	22	3.0%

Other Punctuation

Value	Count	Frequency (%)
/	4492	66.5%
.	1143	16.9%
:	1123	16.6%

Dash Punctuation

Value	Count	Frequency (%)
-	1105	100.0%

Connector Punctuation

Value	Count	Frequency (%)
_	431	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	41740	82.2%
Common	9033	17.8%

Most frequent character per script

Latin

Value	Count	Frequency (%)
t	4402	10.5%
i	3032	7.3%
s	2940	7.0%
h	2809	6.7%
o	2442	5.9%
c	2302	5.5%
e	2218	5.3%
a	2211	5.3%
l	1917	4.6%
g	1817	4.4%
Other values (42)	15650	37.5%

Common

Value	Count	Frequency (%)
/	4492	49.7%
.	1143	12.7%
:	1123	12.4%
-	1105	12.2%
_	431	4.8%
2	196	2.2%
0	153	1.7%
1	127	1.4%
9	54	0.6%
3	47	0.5%
Other values (5)	162	1.8%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	50773	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
/	4492	8.8%
t	4402	8.7%
i	3032	6.0%
s	2940	5.8%
h	2809	5.5%
o	2442	4.8%
c	2302	4.5%
e	2218	4.4%
a	2211	4.4%
l	1917	3.8%
Other values (57)	22008	43.3%

Project Landscape Category
Unsupported

REJECTED UNSUPPORTED

Missing	0
Missing (%)	0.0%
Memory size	8.9 KiB

GitHub Stars
Real number (ℝ)

HIGH CORRELATION SKEWED ZEROS

Distinct	159
Distinct (%)	14.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	118.7569

Minimum	0
Maximum	40028
Zeros	44
Zeros (%)	3.9%
Negative	0
Negative (%)	0.0%
Memory size	8.9 KiB

Quantile statistics

Minimum	0
5-th percentile	1
Q1	1
median	3
Q3	16
95-th percentile	193.7
Maximum	40028
Range	40028
Interquartile range (IQR)	15

Descriptive statistics

Standard deviation	1505.1896
Coefficient of variation (CV)	12.674544
Kurtosis	512.22399
Mean	118.7569
Median Absolute Deviation (MAD)	2
Skewness	21.554905
Sum	133364
Variance	2265595.8
Monotonicity	Decreasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1	316	28.1%
2	130	11.6%
3	88	7.8%
4	66	5.9%
0	44	3.9%
5	39	3.5%
6	28	2.5%
8	24	2.1%
7	20	1.8%
9	18	1.6%
Other values (149)	350	31.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	44	3.9%
1	316	28.1%
2	130	11.6%
3	88	7.8%
4	66	5.9%
5	39	3.5%
6	28	2.5%
7	20	1.8%
8	24	2.1%
9	18	1.6%

Value	Count	Frequency (%)
40028	1	0.1%
24740	1	0.1%
12614	1	0.1%
12393	1	0.1%
2179	1	0.1%
1955	1	0.1%
1947	1	0.1%
1912	1	0.1%
1611	1	0.1%
1592	1	0.1%

GitHub Forks
Real number (ℝ)

HIGH CORRELATION SKEWED ZEROS

Distinct	96
Distinct (%)	8.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	38.960819

Minimum	0
Maximum	16810
Zeros	435
Zeros (%)	38.7%
Negative	0
Negative (%)	0.0%
Memory size	8.9 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	1
Q3	6
95-th percentile	52.9
Maximum	16810
Range	16810
Interquartile range (IQR)	6

Descriptive statistics

Standard deviation	574.99328
Coefficient of variation (CV)	14.758244
Kurtosis	691.24904
Mean	38.960819
Median Absolute Deviation (MAD)	1
Skewness	25.305163
Sum	43753
Variance	330617.27
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	435	38.7%
1	167	14.9%
2	103	9.2%
3	64	5.7%
4	45	4.0%
6	30	2.7%
5	25	2.2%
8	23	2.0%
9	19	1.7%
7	18	1.6%
Other values (86)	194	17.3%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	435	38.7%
1	167	14.9%
2	103	9.2%
3	64	5.7%
4	45	4.0%
5	25	2.2%
6	30	2.7%
7	18	1.6%
8	23	2.0%
9	19	1.7%

Value	Count	Frequency (%)
16810	1	0.1%
8646	1	0.1%
3096	1	0.1%
1332	1	0.1%
1157	1	0.1%
851	1	0.1%
536	1	0.1%
474	1	0.1%
473	1	0.1%
422	1	0.1%

GitHub Subscribers
Real number (ℝ)

HIGH CORRELATION SKEWED ZEROS

Distinct	45
Distinct (%)	4.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	6.2288513

Minimum	0
Maximum	1121
Zeros	52
Zeros (%)	4.6%
Negative	0
Negative (%)	0.0%
Memory size	8.9 KiB

Quantile statistics

Minimum	0
5-th percentile	1
Q1	1
median	2
Q3	4
95-th percentile	15
Maximum	1121
Range	1121
Interquartile range (IQR)	3

Descriptive statistics

Standard deviation	40.027421
Coefficient of variation (CV)	6.4261321
Kurtosis	583.17272
Mean	6.2288513
Median Absolute Deviation (MAD)	1
Skewness	22.780218
Sum	6995
Variance	1602.1945
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=45)

Value	Count	Frequency (%)
1	363	32.3%
2	247	22.0%
3	116	10.3%
4	74	6.6%
0	52	4.6%
5	47	4.2%
6	37	3.3%
7	34	3.0%
8	24	2.1%
9	19	1.7%
Other values (35)	110	9.8%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	52	4.6%
1	363	32.3%
2	247	22.0%
3	116	10.3%
4	74	6.6%
5	47	4.2%
6	37	3.3%
7	34	3.0%
8	24	2.1%
9	19	1.7%

Value	Count	Frequency (%)
1121	1	0.1%
595	1	0.1%
351	1	0.1%
157	1	0.1%
95	1	0.1%
86	1	0.1%
78	1	0.1%
54	1	0.1%
51	1	0.1%
49	1	0.1%

GitHub Open Issues
Real number (ℝ)

HIGH CORRELATION ZEROS

Distinct	79
Distinct (%)	7.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	18.515583

Minimum	0
Maximum	3910
Zeros	674
Zeros (%)	60.0%
Negative	0
Negative (%)	0.0%
Memory size	8.9 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	2
95-th percentile	31.9
Maximum	3910
Range	3910
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	181.21088
Coefficient of variation (CV)	9.7869389
Kurtosis	354.17477
Mean	18.515583
Median Absolute Deviation (MAD)	0
Skewness	18.00145
Sum	20793
Variance	32837.384
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	674	60.0%
1	125	11.1%
2	53	4.7%
3	33	2.9%
4	26	2.3%
6	26	2.3%
8	14	1.2%
5	14	1.2%
7	13	1.2%
19	8	0.7%
Other values (69)	137	12.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	674	60.0%
1	125	11.1%
2	53	4.7%
3	33	2.9%
4	26	2.3%
5	14	1.2%
6	26	2.3%
7	13	1.2%
8	14	1.2%
9	7	0.6%

Value	Count	Frequency (%)
3910	1	0.1%
3661	1	0.1%
2196	1	0.1%
1019	1	0.1%
906	1	0.1%
701	1	0.1%
547	1	0.1%
424	1	0.1%
363	1	0.1%
309	1	0.1%

GitHub Contributors
Real number (ℝ)

HIGH CORRELATION ZEROS

Distinct	38
Distinct (%)	3.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	4.6073019

Minimum	0
Maximum	435
Zeros	27
Zeros (%)	2.4%
Negative	0
Negative (%)	0.0%
Memory size	8.9 KiB

Quantile statistics

Minimum	0
5-th percentile	1
Q1	1
median	1
Q3	3
95-th percentile	10
Maximum	435
Range	435
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	25.443233
Coefficient of variation (CV)	5.5223717
Kurtosis	190.13087
Mean	4.6073019
Median Absolute Deviation (MAD)	0
Skewness	13.186791
Sum	5174
Variance	647.35813
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=38)

Value	Count	Frequency (%)
1	659	58.7%
2	143	12.7%
3	102	9.1%
4	60	5.3%
5	29	2.6%
0	27	2.4%
6	15	1.3%
7	11	1.0%
11	10	0.9%
10	10	0.9%
Other values (28)	57	5.1%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	27	2.4%
1	659	58.7%
2	143	12.7%
3	102	9.1%
4	60	5.3%
5	29	2.6%
6	15	1.3%
7	11	1.0%
8	9	0.8%
9	8	0.7%

Value	Count	Frequency (%)
435	1	0.1%
411	1	0.1%
367	1	0.1%
280	1	0.1%
253	1	0.1%
190	1	0.1%
150	1	0.1%
125	1	0.1%
83	1	0.1%
79	1	0.1%

GitHub License Type
Categorical

MISSING

Distinct	15
Distinct (%)	2.6%
Missing	551
Missing (%)	49.1%
Memory size	8.9 KiB

MIT	211
GPL-3.0	163
NOASSERTION	73
BSD-3-Clause	49
Apache-2.0	29
Other values (10)	47

Length

Max length	18
Median length	12
Mean length	6.7412587
Min length	3

Characters and Unicode

Total characters	3856
Distinct characters	36
Distinct categories	5 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	4 ?
Unique (%)	0.7%

Sample

1st row	BSD-3-Clause
2nd row	BSD-3-Clause
3rd row	Apache-2.0
4th row	MIT
5th row	Apache-2.0

Common Values

Value	Count	Frequency (%)
MIT	211	18.8%
GPL-3.0	163	14.5%
NOASSERTION	73	6.5%
BSD-3-Clause	49	4.4%
Apache-2.0	29	2.6%
CC0-1.0	13	1.2%
AGPL-3.0	9	0.8%
BSD-2-Clause	7	0.6%
GPL-2.0	7	0.6%
LGPL-3.0	4	0.4%
Other values (5)	7	0.6%
(Missing)	551	49.1%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
mit	211	36.9%
gpl-3.0	163	28.5%
noassertion	73	12.8%
bsd-3-clause	49	8.6%
apache-2.0	29	5.1%
cc0-1.0	13	2.3%
agpl-3.0	9	1.6%
bsd-2-clause	7	1.2%
gpl-2.0	7	1.2%
lgpl-3.0	4	0.7%
Other values (5)	7	1.2%

Most occurring characters

Value	Count	Frequency (%)
-	348	9.0%
I	284	7.4%
T	284	7.4%
0	243	6.3%
.	230	6.0%
3	226	5.9%
M	212	5.5%
S	203	5.3%
L	188	4.9%
P	184	4.8%
Other values (26)	1454	37.7%

Most occurring categories

Value	Count	Frequency (%)
Uppercase Letter	2299	59.6%
Decimal Number	530	13.7%
Lowercase Letter	449	11.6%
Dash Punctuation	348	9.0%
Other Punctuation	230	6.0%

Most frequent character per category

Uppercase Letter

Value	Count	Frequency (%)
I	284	12.4%
T	284	12.4%
M	212	9.2%
S	203	8.8%
L	188	8.2%
P	184	8.0%
G	183	8.0%
N	146	6.4%
O	146	6.4%
A	112	4.9%
Other values (7)	357	15.5%

Lowercase Letter

Value	Count	Frequency (%)
e	89	19.8%
a	87	19.4%
l	59	13.1%
s	59	13.1%
u	57	12.7%
c	31	6.9%
h	29	6.5%
p	29	6.5%
i	3	0.7%
r	2	0.4%
Other values (2)	4	0.9%

Decimal Number

Value	Count	Frequency (%)
0	243	45.8%
3	226	42.6%
2	45	8.5%
1	13	2.5%
4	3	0.6%

Dash Punctuation

Value	Count	Frequency (%)
-	348	100.0%

Other Punctuation

Value	Count	Frequency (%)
.	230	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	2748	71.3%
Common	1108	28.7%

Most frequent character per script

Latin

Value	Count	Frequency (%)
I	284	10.3%
T	284	10.3%
M	212	7.7%
S	203	7.4%
L	188	6.8%
P	184	6.7%
G	183	6.7%
N	146	5.3%
O	146	5.3%
A	112	4.1%
Other values (19)	806	29.3%

Common

Value	Count	Frequency (%)
-	348	31.4%
0	243	21.9%
.	230	20.8%
3	226	20.4%
2	45	4.1%
1	13	1.2%
4	3	0.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	3856	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
-	348	9.0%
I	284	7.4%
T	284	7.4%
0	243	6.3%
.	230	6.0%
3	226	5.9%
M	212	5.5%
S	203	5.3%
L	188	4.9%
P	184	4.8%
Other values (26)	1454	37.7%

GitHub Description
Text

MISSING

Distinct	1057
Distinct (%)	98.9%
Missing	54
Missing (%)	4.8%
Memory size	8.9 KiB

Length

Max length	10997
Median length	343
Mean length	134.06642
Min length	7

Characters and Unicode

Total characters	143317
Distinct characters	111
Distinct categories	16 ?
Distinct scripts	5 ?
Distinct blocks	6 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1045 ?
Unique (%)	97.8%

Sample

1st row	Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
2nd row	The fundamental package for scientific computing with Python.
3rd row	Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
4th row	DuckDB is an in-process SQL OLAP Database Management System
5th row	Apache Parquet

Value	Count	Frequency (%)
analysis	950	4.7%
of	791	3.9%
and	673	3.3%
the	669	3.3%
for	654	3.3%
cell	517	2.6%
single-cell	468	2.3%
data	451	2.2%
single	396	2.0%
a	366	1.8%
Other values (4025)	14177	70.5%

Most occurring characters

Value	Count	Frequency (%)
	19189	13.4%
e	12687	8.9%
a	9933	6.9%
i	9819	6.9%
n	8845	6.2%
s	8728	6.1%
l	8228	5.7%
o	7918	5.5%
t	7893	5.5%
r	6159	4.3%
Other values (101)	43918	30.6%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	111625	77.9%
Space Separator	19228	13.4%
Uppercase Letter	7159	5.0%
Other Punctuation	2195	1.5%
Dash Punctuation	1305	0.9%
Decimal Number	1194	0.8%
Close Punctuation	235	0.2%
Open Punctuation	229	0.2%
Math Symbol	51	< 0.1%
Final Punctuation	40	< 0.1%
Other values (6)	56	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	12687	11.4%
a	9933	8.9%
i	9819	8.8%
n	8845	7.9%
s	8728	7.8%
l	8228	7.4%
o	7918	7.1%
t	7893	7.1%
r	6159	5.5%
c	4959	4.4%
Other values (19)	26456	23.7%

Uppercase Letter

Value	Count	Frequency (%)
A	1116	15.6%
S	777	10.9%
R	737	10.3%
C	708	9.9%
N	606	8.5%
T	405	5.7%
D	359	5.0%
P	329	4.6%
I	320	4.5%
M	289	4.0%
Other values (16)	1513	21.1%

Other Punctuation

Value	Count	Frequency (%)
.	909	41.4%
,	624	28.4%
:	186	8.5%
/	173	7.9%
"	169	7.7%
%	40	1.8%
'	37	1.7%
;	18	0.8%
&	14	0.6%
!	7	0.3%
Other values (4)	18	0.8%

Other Symbol

Value	Count	Frequency (%)
▪	6	37.5%
🐟	1	6.2%
🏔	1	6.2%
🌍	1	6.2%
🍱	1	6.2%
🍣	1	6.2%
🦀	1	6.2%
🔬	1	6.2%
🧬	1	6.2%
🌸	1	6.2%

Decimal Number

Value	Count	Frequency (%)
2	262	21.9%
0	252	21.1%
1	207	17.3%
3	92	7.7%
9	86	7.2%
5	70	5.9%
7	70	5.9%
4	62	5.2%
8	48	4.0%
6	45	3.8%

Math Symbol

Value	Count	Frequency (%)
=	22	43.1%
+	15	29.4%
>	9	17.6%
<	4	7.8%
~	1	2.0%

Dash Punctuation

Value	Count	Frequency (%)
-	1302	99.8%
—	2	0.2%
–	1	0.1%

Space Separator

Value	Count	Frequency (%)
	19189	99.8%
	39	0.2%

Close Punctuation

Value	Count	Frequency (%)
)	232	98.7%
]	3	1.3%

Open Punctuation

Value	Count	Frequency (%)
(	226	98.7%
[	3	1.3%

Final Punctuation

Value	Count	Frequency (%)
’	32	80.0%
”	8	20.0%

Connector Punctuation

Value	Count	Frequency (%)
_	29	100.0%

Initial Punctuation

Value	Count	Frequency (%)
“	8	100.0%

Nonspacing Mark

Value	Count	Frequency (%)
️	1	100.0%

Other Letter

Value	Count	Frequency (%)
桜	1	100.0%

Currency Symbol

Value	Count	Frequency (%)
$	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	118783	82.9%
Common	24531	17.1%
Inherited	1	< 0.1%
Han	1	< 0.1%
Greek	1	< 0.1%

Most frequent character per script

Common

Value	Count	Frequency (%)
	19189	78.2%
-	1302	5.3%
.	909	3.7%
,	624	2.5%
2	262	1.1%
0	252	1.0%
)	232	0.9%
(	226	0.9%
1	207	0.8%
:	186	0.8%
Other values (44)	1142	4.7%

Latin

Value	Count	Frequency (%)
e	12687	10.7%
a	9933	8.4%
i	9819	8.3%
n	8845	7.4%
s	8728	7.3%
l	8228	6.9%
o	7918	6.7%
t	7893	6.6%
r	6159	5.2%
c	4959	4.2%
Other values (44)	33614	28.3%

Inherited

Value	Count	Frequency (%)
️	1	100.0%

Han

Value	Count	Frequency (%)
桜	1	100.0%

Greek

Value	Count	Frequency (%)
α	1	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	143204	99.9%
None	54	< 0.1%
Punctuation	51	< 0.1%
Geometric Shapes	6	< 0.1%
VS	1	< 0.1%
CJK	1	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	19189	13.4%
e	12687	8.9%
a	9933	6.9%
i	9819	6.9%
n	8845	6.2%
s	8728	6.1%
l	8228	5.7%
o	7918	5.5%
t	7893	5.5%
r	6159	4.3%
Other values (79)	43805	30.6%

None

Value	Count	Frequency (%)
	39	72.2%
ü	3	5.6%
🐟	1	1.9%
🏔	1	1.9%
🌍	1	1.9%
🍱	1	1.9%
🍣	1	1.9%
🦀	1	1.9%
é	1	1.9%
🔬	1	1.9%
Other values (4)	4	7.4%

Punctuation

Value	Count	Frequency (%)
’	32	62.7%
”	8	15.7%
“	8	15.7%
—	2	3.9%
–	1	2.0%

Geometric Shapes

Value	Count	Frequency (%)
▪	6	100.0%

VS

Value	Count	Frequency (%)
️	1	100.0%

CJK

Value	Count	Frequency (%)
桜	1	100.0%

GitHub Topics
Unsupported

REJECTED UNSUPPORTED

Missing	0
Missing (%)	0.0%
Memory size	8.9 KiB

GitHub Organization
Text

MISSING

Distinct	189
Distinct (%)	63.6%
Missing	826
Missing (%)	73.6%
Memory size	8.9 KiB

Length

Max length	106
Median length	56
Mean length	20.3367
Min length	3

Characters and Unicode

Total characters	6040
Distinct characters	71
Distinct categories	10 ?
Distinct scripts	2 ?
Distinct blocks	3 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	145 ?
Unique (%)	48.8%

Sample

1st row	pandas
2nd row	NumPy
3rd row	The Apache Software Foundation
4th row	DuckDB
5th row	The Apache Software Foundation

Value	Count	Frequency (%)
lab	120	13.9%
bioinformatics	24	2.8%
group	19	2.2%
biology	18	2.1%
institute	16	1.9%
core	16	1.9%
the	15	1.7%
computational	15	1.7%
of	14	1.6%
	14	1.6%
Other values (316)	592	68.6%

Most occurring characters

Value	Count	Frequency (%)
	572	9.5%
a	549	9.1%
e	432	7.2%
i	401	6.6%
o	383	6.3%
n	343	5.7%
r	327	5.4%
t	295	4.9%
s	228	3.8%
l	189	3.1%
Other values (61)	2321	38.4%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	4367	72.3%
Uppercase Letter	1009	16.7%
Space Separator	572	9.5%
Other Punctuation	37	0.6%
Dash Punctuation	28	0.5%
Open Punctuation	9	0.1%
Close Punctuation	9	0.1%
Decimal Number	7	0.1%
Final Punctuation	1	< 0.1%
Connector Punctuation	1	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
a	549	12.6%
e	432	9.9%
i	401	9.2%
o	383	8.8%
n	343	7.9%
r	327	7.5%
t	295	6.8%
s	228	5.2%
l	189	4.3%
b	181	4.1%
Other values (16)	1039	23.8%

Uppercase Letter

Value	Count	Frequency (%)
L	147	14.6%
C	126	12.5%
B	101	10.0%
S	84	8.3%
I	72	7.1%
T	64	6.3%
G	47	4.7%
R	44	4.4%
D	41	4.1%
P	40	4.0%
Other values (16)	243	24.1%

Other Punctuation

Value	Count	Frequency (%)
@	11	29.7%
'	9	24.3%
.	7	18.9%
/	3	8.1%
,	3	8.1%
&	2	5.4%
:	2	5.4%

Decimal Number

Value	Count	Frequency (%)
0	2	28.6%
1	2	28.6%
2	1	14.3%
9	1	14.3%
3	1	14.3%

Dash Punctuation

Value	Count	Frequency (%)
-	25	89.3%
–	3	10.7%

Space Separator

Value	Count	Frequency (%)
	572	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	9	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	9	100.0%

Final Punctuation

Value	Count	Frequency (%)
’	1	100.0%

Connector Punctuation

Value	Count	Frequency (%)
_	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	5376	89.0%
Common	664	11.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
a	549	10.2%
e	432	8.0%
i	401	7.5%
o	383	7.1%
n	343	6.4%
r	327	6.1%
t	295	5.5%
s	228	4.2%
l	189	3.5%
b	181	3.4%
Other values (42)	2048	38.1%

Common

Value	Count	Frequency (%)
	572	86.1%
-	25	3.8%
@	11	1.7%
'	9	1.4%
(	9	1.4%
)	9	1.4%
.	7	1.1%
/	3	0.5%
,	3	0.5%
–	3	0.5%
Other values (9)	13	2.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	6035	99.9%
Punctuation	4	0.1%
None	1	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	572	9.5%
a	549	9.1%
e	432	7.2%
i	401	6.6%
o	383	6.3%
n	343	5.7%
r	327	5.4%
t	295	4.9%
s	228	3.8%
l	189	3.1%
Other values (58)	2316	38.4%

Punctuation

Value	Count	Frequency (%)
–	3	75.0%
’	1	25.0%

None

Value	Count	Frequency (%)
á	1	100.0%

GitHub Network Count
Real number (ℝ)

HIGH CORRELATION SKEWED ZEROS

Distinct	97
Distinct (%)	8.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	39.023152

Minimum	0
Maximum	16810
Zeros	435
Zeros (%)	38.7%
Negative	0
Negative (%)	0.0%
Memory size	8.9 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	1
Q3	6
95-th percentile	54.8
Maximum	16810
Range	16810
Interquartile range (IQR)	6

Descriptive statistics

Standard deviation	574.99266
Coefficient of variation (CV)	14.734654
Kurtosis	691.24099
Mean	39.023152
Median Absolute Deviation (MAD)	1
Skewness	25.304919
Sum	43823
Variance	330616.56
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	435	38.7%
1	166	14.8%
2	103	9.2%
3	64	5.7%
4	45	4.0%
6	29	2.6%
5	25	2.2%
8	23	2.0%
9	20	1.8%
7	18	1.6%
Other values (87)	195	17.4%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	435	38.7%
1	166	14.8%
2	103	9.2%
3	64	5.7%
4	45	4.0%
5	25	2.2%
6	29	2.6%
7	18	1.6%
8	23	2.0%
9	20	1.8%

Value	Count	Frequency (%)
16810	1	0.1%
8646	1	0.1%
3096	1	0.1%
1332	1	0.1%
1157	1	0.1%
851	1	0.1%
536	1	0.1%
474	1	0.1%
473	1	0.1%
422	1	0.1%

GitHub Detected Languages
Unsupported

REJECTED UNSUPPORTED

Missing	0
Missing (%)	0.0%
Memory size	8.9 KiB

Date Created
Date

UNIQUE

Distinct	1123
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Memory size	8.9 KiB

Minimum	2010-08-24 01:37:33+00:00
Maximum	2023-10-10 14:24:36+00:00

Histogram

Histogram with fixed size bins (bins=50)

Date Most Recent Commit
Date

UNIQUE

Distinct	1123
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Memory size	8.9 KiB

Minimum	2011-06-21 13:44:00+00:00
Maximum	2023-10-16 15:44:24+00:00

Histogram

Histogram with fixed size bins (bins=50)

Duration Created to Most Recent Commit
Unsupported

REJECTED UNSUPPORTED

Missing	0
Missing (%)	0.0%
Memory size	8.9 KiB

Duration Created to Now
Unsupported

REJECTED UNSUPPORTED

Missing	0
Missing (%)	0.0%
Memory size	8.9 KiB

Duration Most Recent Commit to Now
Unsupported

REJECTED UNSUPPORTED

Missing	0
Missing (%)	0.0%
Memory size	8.9 KiB

Repository Size (KB)
Real number (ℝ)

Distinct	1010
Distinct (%)	89.9%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	75420.917

Minimum	1
Maximum	1922901
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	8.9 KiB

Quantile statistics

Minimum	1
5-th percentile	16.1
Q1	688
median	10748
Q3	65047
95-th percentile	360260.2
Maximum	1922901
Range	1922900
Interquartile range (IQR)	64359

Descriptive statistics

Standard deviation	186112.03
Coefficient of variation (CV)	2.4676447
Kurtosis	39.984742
Mean	75420.917
Median Absolute Deviation (MAD)	10707
Skewness	5.5454072
Sum	84697690
Variance	3.4637687 × 10¹⁰
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
6	10	0.9%
26	7	0.6%
13	6	0.5%
3	6	0.5%
67	5	0.4%
16	5	0.4%
24	5	0.4%
22	4	0.4%
30	4	0.4%
9	4	0.4%
Other values (1000)	1067	95.0%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	2	0.2%
2	2	0.2%
3	6	0.5%
4	3	0.3%
5	3	0.3%
6	10	0.9%
7	3	0.3%
8	3	0.3%
9	4	0.4%
10	1	0.1%

Value	Count	Frequency (%)
1922901	1	0.1%
1921765	1	0.1%
1792729	1	0.1%
1590023	1	0.1%
1496132	1	0.1%
1461986	1	0.1%
1447466	1	0.1%
1293566	1	0.1%
1245051	1	0.1%
1068214	1	0.1%

GitHub Repo Archived
Boolean

IMBALANCE

Distinct	2
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Memory size	1.2 KiB

False	1110
True	13

Common Values (Table)
Common Values (Plot)

Value	Count	Frequency (%)
False	1110	98.8%
True	13	1.2%

Duration Created to Now in Years
Real number (ℝ)

HIGH CORRELATION

Distinct	917
Distinct (%)	81.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	3.846063

Minimum	0.016438356
Maximum	13.153425
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	8.9 KiB

Quantile statistics

Minimum	0.016438356
5-th percentile	0.64136986
Q1	2.2383562
median	3.5424658
Q3	5.2767123
95-th percentile	7.8227397
Maximum	13.153425
Range	13.136986
Interquartile range (IQR)	3.0383562

Descriptive statistics

Standard deviation	2.2608857
Coefficient of variation (CV)	0.58784417
Kurtosis	0.82613703
Mean	3.846063
Median Absolute Deviation (MAD)	1.4767123
Skewness	0.76387459
Sum	4319.1288
Variance	5.1116042
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
2.695890411	4	0.4%
4.142465753	4	0.4%
2.410958904	3	0.3%
2.252054795	3	0.3%
0.7369863014	3	0.3%
3.923287671	3	0.3%
5.520547945	3	0.3%
4.383561644	3	0.3%
3.682191781	3	0.3%
3.504109589	3	0.3%
Other values (907)	1091	97.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0.01643835616	1	0.1%
0.09315068493	1	0.1%
0.09589041096	1	0.1%
0.1452054795	1	0.1%
0.1506849315	1	0.1%
0.1643835616	1	0.1%
0.1726027397	2	0.2%
0.1753424658	2	0.2%
0.2219178082	2	0.2%
0.2328767123	1	0.1%

Value	Count	Frequency (%)
13.15342466	1	0.1%
13.09589041	1	0.1%
13.07945205	1	0.1%
12.53972603	1	0.1%
12.21643836	1	0.1%
12.1369863	1	0.1%
12.07671233	1	0.1%
11.28767123	1	0.1%
11.09863014	1	0.1%
10.60821918	1	0.1%

Negative Duration Most Recent Commit to Now
Unsupported

REJECTED UNSUPPORTED

Missing	0
Missing (%)	0.0%
Memory size	8.9 KiB

category
Categorical

IMBALANCE

Distinct	5
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Memory size	8.9 KiB

related-tools-github-query-result	1004
cytomining-ecosystem-adjacent-tools	100
cytomining-ecosystem-relevant-open-source	8
microscopy-analysis-tools	7
loi-focus	4

Length

Max length	41
Median length	33
Mean length	33.099733
Min length	9

Characters and Unicode

Total characters	37171
Distinct characters	23
Distinct categories	2 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	cytomining-ecosystem-relevant-open-source
2nd row	cytomining-ecosystem-relevant-open-source
3rd row	cytomining-ecosystem-relevant-open-source
4th row	cytomining-ecosystem-relevant-open-source
5th row	cytomining-ecosystem-relevant-open-source

Common Values

Value	Count	Frequency (%)
related-tools-github-query-result	1004	89.4%
cytomining-ecosystem-adjacent-tools	100	8.9%
cytomining-ecosystem-relevant-open-source	8	0.7%
microscopy-analysis-tools	7	0.6%
loi-focus	4	0.4%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
related-tools-github-query-result	1004	89.4%
cytomining-ecosystem-adjacent-tools	100	8.9%
cytomining-ecosystem-relevant-open-source	8	0.7%
microscopy-analysis-tools	7	0.6%
loi-focus	4	0.4%

Most occurring characters

Value	Count	Frequency (%)
t	4447	12.0%
-	4366	11.7%
e	4364	11.7%
l	3138	8.4%
r	3035	8.2%
u	3024	8.1%
o	2476	6.7%
s	2364	6.4%
i	1238	3.3%
y	1234	3.3%
Other values (13)	7485	20.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	32805	88.3%
Dash Punctuation	4366	11.7%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
t	4447	13.6%
e	4364	13.3%
l	3138	9.6%
r	3035	9.3%
u	3024	9.2%
o	2476	7.5%
s	2364	7.2%
i	1238	3.8%
y	1234	3.8%
a	1226	3.7%
Other values (12)	6259	19.1%

Dash Punctuation

Value	Count	Frequency (%)
-	4366	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	32805	88.3%
Common	4366	11.7%

Most frequent character per script

Latin

Value	Count	Frequency (%)
t	4447	13.6%
e	4364	13.3%
l	3138	9.6%
r	3035	9.3%
u	3024	9.2%
o	2476	7.5%
s	2364	7.2%
i	1238	3.8%
y	1234	3.8%
a	1226	3.7%
Other values (12)	6259	19.1%

Common

Value	Count	Frequency (%)
-	4366	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	37171	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
t	4447	12.0%
-	4366	11.7%
e	4364	11.7%
l	3138	8.4%
r	3035	8.2%
u	3024	8.1%
o	2476	6.7%
s	2364	6.4%
i	1238	3.3%
y	1234	3.3%
Other values (13)	7485	20.1%

Auto

Heatmap
Table

	GitHub Repository ID	GitHub Stars	GitHub Forks	GitHub Subscribers	GitHub Open Issues	GitHub Contributors	GitHub Network Count	Repository Size (KB)	Duration Created to Now in Years	GitHub License Type	GitHub Repo Archived	category
GitHub Repository ID	1.000	-0.365	-0.415	-0.447	-0.305	-0.248	-0.415	-0.140	-1.000	0.087	0.077	0.214
GitHub Stars	-0.365	1.000	0.819	0.644	0.685	0.536	0.817	0.398	0.365	0.000	0.000	0.404
GitHub Forks	-0.415	0.819	1.000	0.628	0.663	0.553	0.999	0.382	0.415	0.000	0.000	0.348
GitHub Subscribers	-0.447	0.644	0.628	1.000	0.589	0.559	0.625	0.350	0.447	0.000	0.000	0.348
GitHub Open Issues	-0.305	0.685	0.663	0.589	1.000	0.548	0.661	0.284	0.305	0.000	0.000	0.361
GitHub Contributors	-0.248	0.536	0.553	0.559	0.548	1.000	0.553	0.393	0.248	0.000	0.000	0.487
GitHub Network Count	-0.415	0.817	0.999	0.625	0.661	0.553	1.000	0.382	0.415	0.000	0.000	0.348
Repository Size (KB)	-0.140	0.398	0.382	0.350	0.284	0.393	0.382	1.000	0.140	0.000	0.000	0.046
Duration Created to Now in Years	-1.000	0.365	0.415	0.447	0.305	0.248	0.415	0.140	1.000	0.104	0.101	0.297
GitHub License Type	0.087	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.104	1.000	0.000	0.229
GitHub Repo Archived	0.077	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.101	0.000	1.000	0.000
category	0.214	0.404	0.348	0.348	0.361	0.487	0.348	0.046	0.297	0.229	0.000	1.000

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

First rows
Last rows

	Project Name	GitHub Repository ID	Project Homepage	Project Repo URL	Project Landscape Category	GitHub Stars	GitHub Forks	GitHub Subscribers	GitHub Open Issues	GitHub Contributors	GitHub License Type	GitHub Description	GitHub Topics	GitHub Organization	GitHub Network Count	GitHub Detected Languages	Date Created	Date Most Recent Commit	Duration Created to Most Recent Commit	Duration Created to Now	Duration Most Recent Commit to Now	Repository Size (KB)	GitHub Repo Archived	Duration Created to Now in Years	Negative Duration Most Recent Commit to Now	category
0	pandas	858127	https://pandas.pydata.org	https://github.com/pandas-dev/pandas	[cytomining-ecosystem-relevant-open-source]	40028	16810	1121	3661	411	BSD-3-Clause	Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more	[alignment, data-analysis, data-science, flexible, pandas, python]	pandas	16810	{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': 386224.0, 'C#': None, 'C++': None, 'CMake': None, 'CSS': 6804.0, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': 1277513.0, 'D': None, 'Dockerfile': 5751.0, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': 457000.0, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': 10664.0, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 20323714.0, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': 14392.0, 'Singularity': None, 'Smarty': 8486.0, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': 1196.0, 'eC': None, 'sed': None}	2010-08-24 01:37:33+00:00	2023-10-16 13:28:00+00:00	4801 days 11:50:27	4801 days 16:10:06.376566	0 days 02:21:00.548031	334787	False	13.153425	-1 days +21:38:59.451969	cytomining-ecosystem-relevant-open-source
1	numpy	908607	https://numpy.org	https://github.com/numpy/numpy	[cytomining-ecosystem-relevant-open-source]	24740	8646	595	2196	435	BSD-3-Clause	The fundamental package for scientific computing with Python.	[numpy, python]	NumPy	8646	{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': 6220071.0, 'C#': None, 'C++': 205725.0, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': 151476.0, 'D': 19.0, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': 3787.0, 'Fortran': 27683.0, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': 1697.0, 'Mako': None, 'Mercury': None, 'Meson': 88875.0, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 10458450.0, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': 17058.0, 'Singularity': None, 'Smarty': 4129.0, 'Stan': None, 'Standard ML': None, 'Starlark': 1842.0, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': 5699.0}	2010-09-13 23:02:39+00:00	2023-10-16 14:25:39+00:00	4780 days 15:23:00	4780 days 18:45:00.376566	0 days 01:23:21.548031	131902	False	13.095890	-1 days +22:36:38.451969	cytomining-ecosystem-relevant-open-source
2	arrow	51905353	https://arrow.apache.org/	https://github.com/apache/arrow	[cytomining-ecosystem-relevant-open-source]	12614	3096	351	3910	367	Apache-2.0	Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing	[arrow]	The Apache Software Foundation	3096	{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': 3709.0, 'Batchfile': 32824.0, 'C': 1507496.0, 'C#': 1505684.0, 'C++': 26864776.0, 'CMake': 732440.0, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': 1708430.0, 'D': None, 'Dockerfile': 135006.0, 'Emacs Lisp': 1064.0, 'Forth': None, 'Fortran': None, 'FreeMarker': 2312.0, 'Gnuplot': None, 'Go': 5619807.0, 'Groovy': None, 'HCL': None, 'HTML': 5604.0, 'Hack': None, 'ImageJ Macro': None, 'Java': 7353737.0, 'JavaScript': 128685.0, 'Jinja': 21888.0, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': 8771.0, 'M': None, 'M4': None, 'MATLAB': 722935.0, 'Makefile': 32659.0, 'Mako': None, 'Mercury': None, 'Meson': 62865.0, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': 11472.0, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 3288210.0, 'QMake': None, 'R': 1698163.0, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': 1794908.0, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': 411936.0, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': 461913.0, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': 34537.0, 'TypeScript': 1108325.0, 'VBScript': None, 'Vala': 24798.0, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': 1256.0}	2016-02-17 08:00:23+00:00	2023-10-16 15:32:21+00:00	2798 days 07:31:58	2798 days 09:47:16.376566	0 days 00:16:39.548031	170924	False	7.665753	-1 days +23:43:20.451969	cytomining-ecosystem-relevant-open-source
3	duckdb	138754790	http://www.duckdb.org	https://github.com/duckdb/duckdb	[cytomining-ecosystem-relevant-open-source]	12393	1157	157	309	253	MIT	DuckDB is an in-process SQL OLAP Database Management System	[analytics, database, embedded-database, olap, sql]	DuckDB	1157	{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': 1761733.0, 'C#': None, 'C++': 33576510.0, 'CMake': 146234.0, 'CSS': 182.0, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': 343518.0, 'JavaScript': 12990.0, 'Jinja': None, 'Julia': 250635.0, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': 14649.0, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 1407442.0, 'QMake': None, 'R': 1693.0, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': 26187.0, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': 282562.0, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}	2018-06-26 15:04:45+00:00	2023-10-16 14:58:09+00:00	1937 days 23:53:24	1938 days 02:42:54.376566	0 days 00:50:51.548031	227662	False	5.309589	-1 days +23:09:08.451969	cytomining-ecosystem-relevant-open-source
4	parquet-mr	20675636		https://github.com/apache/parquet-mr	[cytomining-ecosystem-relevant-open-source]	2179	1332	95	130	190	Apache-2.0	Apache Parquet	[big-data, java, parquet]	The Apache Software Foundation	1332	{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': 5923431.0, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 14771.0, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': 8436.0, 'Scheme': None, 'Scilab': None, 'Shell': 14860.0, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': 10354.0, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}	2014-06-10 07:00:07+00:00	2023-10-16 15:19:17+00:00	3415 days 08:19:10	3415 days 10:47:32.376566	0 days 00:29:43.548031	18475	False	9.356164	-1 days +23:30:16.451969	cytomining-ecosystem-relevant-open-source
5	snakemake	212840200	https://snakemake.readthedocs.io	https://github.com/snakemake/snakemake	[cytomining-ecosystem-relevant-open-source]	1955	474	22	906	280	MIT	This is the development home of the workflow management system Snakemake. For general information, see	[reproducibility, snakemake, workflow-management]	Snakemake	474	{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': 1346.0, 'C#': None, 'C++': None, 'CMake': None, 'CSS': 3033.0, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': 1727.0, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': 3647966.0, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': 43973.0, 'Jinja': 6950.0, 'Julia': 334.0, 'Jupyter Notebook': 4389.0, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': 4400.0, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': 5.0, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 1336865.0, 'QMake': None, 'R': 786.0, 'Raku': None, 'Reason': None, 'Rebol': 6.0, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': 6.0, 'Ruby': None, 'Rust': 3617.0, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': 5149.0, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': 5722.0, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}	2019-10-04 14:58:11+00:00	2023-10-16 09:50:35+00:00	1472 days 18:52:24	1473 days 02:49:28.376566	0 days 05:58:25.548031	92014	False	4.035616	-1 days +18:01:34.451969	cytomining-ecosystem-relevant-open-source
6	seurat	35927665	http://www.satijalab.org/seurat	https://github.com/satijalab/seurat	[cytomining-ecosystem-adjacent-tools]	1947	851	78	229	83	NOASSERTION	R toolkit for single cell genomics	[cran, human-cell-atlas, single-cell-genomics, single-cell-rna-seq]	None	851	{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': 166.0, 'C#': None, 'C++': 103887.0, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': None, 'QMake': None, 'R': 1272988.0, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': 942.0, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}	2015-05-20 05:23:02+00:00	2023-10-03 21:28:12+00:00	3058 days 16:05:10	3071 days 12:24:37.376566	12 days 18:20:48.548031	22722	False	8.413699	-13 days +05:39:11.451969	cytomining-ecosystem-adjacent-tools
7	napari	144513571	https://napari.org	https://github.com/napari/napari	[microscopy-analysis-tools]	1912	389	49	1019	150	BSD-3-Clause	napari: a fast, interactive, multi-dimensional image viewer for python	[napari, numpy, python, visualization]	napari	389	{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': 465.0, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': 6722.0, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 4637636.0, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': 2846.0, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': 1221.0, 'Singularity': 95.0, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}	2018-08-13 01:12:28+00:00	2023-10-16 15:20:03+00:00	1890 days 14:07:35	1890 days 16:35:11.376566	0 days 00:28:57.548031	76651	False	5.178082	-1 days +23:31:02.451969	microscopy-analysis-tools
8	scanpy	80342493	https://scanpy.readthedocs.io	https://github.com/scverse/scanpy	[cytomining-ecosystem-adjacent-tools]	1611	536	51	547	125	BSD-3-Clause	Single-cell analysis in Python. Scales to >1M cells.	[anndata, bioinformatics, data-science, machine-learning, python, scanpy, scverse, transcriptomics, visualize-data]	scverse	536	{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 1292128.0, 'QMake': None, 'R': 2315.0, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}	2017-01-29 11:31:11+00:00	2023-10-16 15:40:34+00:00	2451 days 04:09:23	2451 days 06:16:28.376566	0 days 00:08:26.548031	39269	False	6.715068	-1 days +23:51:33.451969	cytomining-ecosystem-adjacent-tools
9	STAR	17778869		https://github.com/alexdobin/STAR	[cytomining-ecosystem-adjacent-tools]	1592	473	86	701	34	MIT	RNA-seq aligner	[]	None	473	{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': 24345.0, 'Batchfile': None, 'C': 2350683.0, 'C#': None, 'C++': 1210764.0, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': 676.0, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': 1006.0, 'Makefile': 25383.0, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': None, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': 11364.0, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': 295.0, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': 119547.0, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}	2014-03-15 15:47:05+00:00	2023-09-08 12:23:35+00:00	3463 days 20:36:30	3502 days 02:00:34.376566	38 days 03:25:25.548031	541229	False	9.594521	-39 days +20:34:34.451969	cytomining-ecosystem-adjacent-tools

	Project Name	GitHub Repository ID	Project Homepage	Project Repo URL	Project Landscape Category	GitHub Subscribers	GitHub Contributors	GitHub License Type	GitHub Description	GitHub Topics	GitHub Organization	GitHub Detected Languages	Date Created	Date Most Recent Commit	Duration Created to Most Recent Commit	Duration Created to Now	Duration Most Recent Commit to Now	Repository Size (KB)	GitHub Repo Archived	Duration Created to Now in Years	Negative Duration Most Recent Commit to Now	category
1113	BloodVesselImageAnalysis	323621577		https://github.com/RoopaMadhu/BloodVesselImageAnalysis	[related-tools-github-query-result]	1	1	None	Spatiographic projections of biological systems	[]	None	{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': 188297.0, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': None, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}	2020-12-22 12:36:17+00:00	2020-12-22 14:22:59+00:00	0 days 01:46:42	1028 days 05:11:22.376566	1028 days 01:26:01.548031	576	False	2.816438	-1029 days +22:33:58.451969	related-tools-github-query-result
1114	SickleCellDataAnalysis	298584969	None	https://github.com/ridz46/SickleCellDataAnalysis	[related-tools-github-query-result]	1	1	None	This repository contains the codes used for image processing and further downstream data analysis for the project on the development of a point-of-care diagnostic test for sickle cell disease. This repository is maintained by the Microfluidics & Biological Physics group, Department of Biosciences & Bioengineering, Indian Institute of Technology Bombay, Mumbai, India.	[]	None	{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': None, 'QMake': None, 'R': 4373.0, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}	2020-09-25 13:45:27+00:00	2020-09-28 08:55:24+00:00	2 days 19:09:57	1116 days 04:02:12.376566	1113 days 06:53:36.548031	17	False	3.057534	-1114 days +17:06:23.451969	related-tools-github-query-result
1115	Data-Analysis-AMATH-482	245942374	None	https://github.com/priyanshir/Data-Analysis-AMATH-482	[related-tools-github-query-result]	1	1	None	Exploratory and objective data analysis methods applied to the physical, engineering, and biological sciences. Brief review of statistical methods and their computational implementation for studying time series analysis, spectral analysis, filtering methods, principal component analysis, orthogonal mode decomposition, and image processing and compression	[]	None	{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': 703573.0, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': 33375.0, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': None, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}	2020-03-09 04:17:02+00:00	2020-03-14 22:41:50+00:00	5 days 18:24:48	1316 days 13:30:37.376566	1310 days 17:07:10.548031	2832	False	3.605479	-1311 days +06:52:49.451969	related-tools-github-query-result
1116	Aging-Cell-Morphology-Cell-transformations-and-image-processing	235024426	None	https://github.com/sumisingh/Aging-Cell-Morphology-Cell-transformations-and-image-processing	[related-tools-github-query-result]	1	1	None	Identifying cellular transformations associated with aging using image processing	[]	None	{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': 8130261.0, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': None, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}	2020-01-20 05:18:57+00:00	2020-01-20 05:29:56+00:00	0 days 00:10:59	1365 days 12:28:42.376566	1365 days 10:19:04.548031	27874	False	3.739726	-1366 days +13:40:55.451969	related-tools-github-query-result
1117	CellMorphology	148259959		https://github.com/KnightofDawn/CellMorphology	[related-tools-github-query-result]	1	1	None	Python code to identify/analyze cells from microscopic stack images.	[]	None	{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 65906.0, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}	2018-09-11 04:25:09+00:00	2018-09-10 20:41:28+00:00	-1 days +16:16:19	1861 days 13:22:30.376566	1861 days 19:07:32.548031	161	False	5.098630	-1862 days +04:52:27.451969	related-tools-github-query-result
1118	ImagingCells	137526283	None	https://github.com/jesnyder/ImagingCells	[related-tools-github-query-result]	1	1	None	Scripts to analyze cell number and morphologies using images.	[]	None	{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': 1523.0, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': None, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}	2018-06-15 19:50:00+00:00	2018-08-31 19:21:33+00:00	76 days 23:31:33	1948 days 21:57:39.376566	1871 days 20:27:27.548031	1	False	5.336986	-1872 days +03:32:32.451969	related-tools-github-query-result
1119	course-bia	119301640	None	https://github.com/denzf/course-bia	[related-tools-github-query-result]	1	1	MIT	Code examples for the course of Biological Image Analysis	[]	None	{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 6010.0, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}	2018-01-28 21:58:13+00:00	2018-01-24 03:22:19+00:00	-5 days +05:24:06	2086 days 19:49:26.376566	2091 days 12:26:41.548031	203	False	5.715068	-2092 days +11:33:18.451969	related-tools-github-query-result
1120	Cell-virulence-Detection-using-Image-Processing	163268436	None	https://github.com/arushigupta148/Cell-virulence-Detection-using-Image-Processing	[related-tools-github-query-result]	0	1	None	Designed an automated tool to find the thickness of multiple cell capsules from images using morphological operations to generate plots of cell size vs capsular thickness, simplifying detection of virulence in yeast cells for mycologists	[]	None	{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': None, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': 12989.0, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}	2018-12-27 08:27:06+00:00	2019-05-12 23:09:02+00:00	136 days 14:41:56	1754 days 09:20:33.376566	1617 days 16:39:58.548031	1751	False	4.805479	-1618 days +07:20:01.451969	related-tools-github-query-result
1121	Image-analysis	152904377		https://github.com/dguin/Image-analysis	[related-tools-github-query-result]	0	1	None	The repository contains code to analyze videos where each frame is a snapshot of the cellular status as a function of time. The program includes subroutines for segmentation protocols to pick a cell and differentiate it from the background when the signal to noise is low. The protocol docx explains what each code does and explains the order in which they must be run. As is the program analyzes FRET data from a cell, where the temperature increases as a function of time and one can evaluate the changes in the cell morphology as the cell is under heat stress	[]	None	{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': 40670.0, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': None, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}	2018-10-13 18:53:42+00:00	2018-10-13 19:32:22+00:00	0 days 00:38:40	1828 days 22:53:57.376566	1828 days 20:16:38.548031	26	False	5.008219	-1829 days +03:43:21.451969	related-tools-github-query-result
1122	oct-image-analysis	125785309		https://github.com/ricster101/oct-image-analysis	[related-tools-github-query-result]	0	1	None	Work developed with Adriana Costa during the course of Computer Vision and Biological Perception aiming to discover differences in mice retina	[]	None	{'AMPL': None, 'AspectJ': None, 'Assembly': None, 'Awk': None, 'Batchfile': None, 'C': None, 'C#': None, 'C++': None, 'CMake': None, 'CSS': None, 'Clojure': None, 'Common Workflow Language': None, 'Cuda': None, 'Cython': None, 'D': None, 'Dockerfile': None, 'Emacs Lisp': None, 'Forth': None, 'Fortran': None, 'FreeMarker': None, 'Gnuplot': None, 'Go': None, 'Groovy': None, 'HCL': None, 'HTML': None, 'Hack': None, 'ImageJ Macro': None, 'Java': None, 'JavaScript': None, 'Jinja': None, 'Julia': None, 'Jupyter Notebook': None, 'Kotlin': None, 'Lua': None, 'M': None, 'M4': None, 'MATLAB': 22755.0, 'Makefile': None, 'Mako': None, 'Mercury': None, 'Meson': None, 'Nextflow': None, 'Objective-C': None, 'Objective-C++': None, 'OpenEdge ABL': None, 'PHP': None, 'PLpgSQL': None, 'POV-Ray SDL': None, 'Perl': None, 'Perl 6': None, 'PostScript': None, 'PowerShell': None, 'Processing': None, 'Procfile': None, 'Python': None, 'QMake': None, 'R': None, 'Raku': None, 'Reason': None, 'Rebol': None, 'Ren'Py': None, 'Rich Text Format': None, 'Roff': None, 'Ruby': None, 'Rust': None, 'SCSS': None, 'Scala': None, 'Scheme': None, 'Scilab': None, 'Shell': None, 'Singularity': None, 'Smarty': None, 'Stan': None, 'Standard ML': None, 'Starlark': None, 'Swift': None, 'TSQL': None, 'TeX': None, 'Terra': None, 'Thrift': None, 'TypeScript': None, 'VBScript': None, 'Vala': None, 'Vim Script': None, 'Visual Basic .NET': None, 'Vue': None, 'WDL': None, 'XSLT': None, 'eC': None, 'sed': None}	2018-03-19 01:29:32+00:00	2018-03-19 03:31:25+00:00	0 days 02:01:53	2037 days 16:18:07.376566	2037 days 12:17:35.548031	16	False	5.580822	-2038 days +11:42:24.451969	related-tools-github-query-result

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